Multiple-Input-Multiple-Output (MIMO) is an advanced antenna technique utilized in wireless systems (e.g., cellular communications networks) to improve spectral efficiency and thereby boost overall system capacity. For MIMO, a commonly known notation of (NT×NR) is used to represent the MIMO configuration in terms the number of transmit antennas (NT) and the number of receive antennas (NR). Common MIMO configurations used or currently discussed for various technologies are: (2×1), (1×2), (2×2), (4×2), (8×2), (2×4), (4×4), (8×4), and (8×8). The MIMO configurations represented by (2×1) and (1×2) are special cases of MIMO, and they correspond to transmit diversity and receive diversity, respectively.
It is well known that MIMO techniques can significantly increase the data carrying capacity of wireless systems. For these reasons, MIMO is an integral part of the 3rd and 4th generation wireless systems. Future 5th generation systems will also employ MIMO systems, also called massive MIMO systems (e.g., hundreds of antennas at the transmitter side and/or the receiver side). Typically with a (NT×NR) MIMO configuration, the peak data rate multiplies with a factor of NT over single antenna systems in a rich scattering environment.
FIG. 1 illustrates a MIMO transmitter 10 of a typical MIMO system with NT transmit antennas. In this example, there are NT transport blocks 12. However, in some cases, the number of transport blocks 12 can be less than NT. Cyclic Redundancy Check (CRC) bits are added to each transport block 12, and the resulting transport blocks 12 including the CRC bits are passed to corresponding channel encoders 14. The channel encoders 14 add parity bits to protect the data. The resulting encoded data streams are passed through interleavers and modulators 16. An adaptive controller 18 adaptively controls the size of the interleavers by puncturing to increase the data rate. The adaptation is done by using information from a feedback channel received from the corresponding MIMO receiver (not shown) of the MIMO system. The information from the feedback channel may include, for example, Channel State Information (CSI) sent by the MIMO receiver. The interleaved data is passed through a symbol mapper, or modulator (not shown). The symbol mapper is also controlled by the adaptive controller 18. The modulated data streams are passed through a layer mapper 20 and a precoder 22. The resultant streams are then passed through Inverse Fast Fourier Transform (IFFT) blocks 24. Note that the IFFT blocks 24 are necessary for some communications systems that implement Orthogonal Frequency Division Multiple Access (OFDMA) as the access technology (e.g., Long Term Evolution (LTE)/LTE-Advanced (LTE-A), Wi-max). For other communications systems that do not implement OFDMA (e.g., communications that implement CDMA as the access technology such as, e.g., High Speed Downlink Packet Access (HSDPA), etc.), the IFFT blocks 24 are replaced by spreading/scrambling blocks. Finally, the encoded streams are then transmitted through the respective antennas.
FIG. 2 illustrates a MIMO receiver 26 of a typical MIMO system with NR receive antennas. After Fast Fourier Transform (FFT) blocks 28 process the inputs from the NR receive antennas, a MIMO detector 30 is used to remove the multi antenna interference. De-mappers 32 are then used to compute the bit log likelihood ratios from the outputs of the MIMO detector 30, which are in the symbol domain. The bit streams are then de-interleaved by corresponding de-interleavers 34 and passed to channel decoders 36. CRC blocks 38 perform a CRC check on the output of the channel decoders 36. If the CRC is passed, the transport block is considered to be passed and an Acknowledgement (ACK) is sent back to the MIMO transmitter 10 via a feedback channel. If the CRC is failed, then a Negative Acknowledgement (NACK) is sent back to the MIMO transmitter 10 using the feedback channel. A channel estimator 40 operates to estimate the MIMO channel between the MIMO transmitter 10 and the MIMO receiver 26. The resulting channel estimate is utilized by the MIMO detector 30.
The MIMO detector 30 is needed to remove or reduce the inter-stream interference when spatial multiplexing is used. There are different types of MIMO detectors. Typically, MIMO detectors can be classified into two categories or types, namely, filter-based MIMO detectors which may be linear or non-linear (sometimes referred to herein as Type A MIMO detectors) and list-based MIMO detectors (sometimes referred to herein as Type B MIMO detectors).
Filter-based MIMO detectors (i.e., Type A MIMO detectors) include all MIMO detectors that utilize a filter-based technique. Some examples of filter-based MIMO detectors are: linear MIMO detectors such as, for example, Zero-Forcing (ZF) MIMO detectors and Minimum Mean Square Error (MMSE) MIMO detectors and non-linear MIMO detectors such as, for example, decision feedback MIMO detectors, nulling-cancelling MIMO detectors, and variants relying on Successive Interference Cancellation (SIC). Conversely, list-based MIMO detectors (i.e., Type B MIMO detectors) include all MIMO detectors that utilize a list-based technique. Some examples of list-based MIMO detectors are: Maximum Likelihood (ML) MIMO detectors, Maximum A Posteriori Probability (MAP) MIMO detectors, and reduced list size MIMO detectors such as, for example, sphere decoding MIMO detectors, list sphere decoding MIMO detectors, and variants relying on reduced list size ML or MAP MIMO detectors.
It is well known that Type B MIMO detectors (e.g., ML MIMO detectors) have better performance than Type A MIMO detectors (e.g., MMSE MIMO detectors) for fixed modulation and transport block size (code rate) (see, for example, J. Proakis et al., “Digital Communications,” 5th edition, McGraw-Hill Science/Engineering/Math, Nov. 6, 2007). However, in wireless communications systems using adaptive rank, precoding index, modulation, and code rate, Type B MIMO detectors typically do not have better performance than Type A MIMO detectors. As such, Type A MIMO detectors, rather than Type B MIMO detectors, are commonly used in wireless communications systems.
In particular, in a wireless communications system, the performance is impacted by channel variations. For MIMO receivers employing a MMSE MIMO detector (i.e., a MMSE receiver), this performance loss can be minimized by using adaptive rank, precoding index, modulation, and code rate. This adaptation is based on feedback of Channel Quality Indicator (CQI) from the MIMO receiver to the MIMO transmitter. Computation of CQI for Type A MIMO receivers is performed using well-known mathematical expressions. However, no such expressions are known for computing CQI for a Type B MIMO receiver. As such, Type B MIMO receivers are not currently used in wireless communications systems in which adaptive rank, precoding index, modulation, and code rate are desired. In other words, because there is no known expression for computing CQI for a Type B MIMO receiver, a wireless MIMO communications system using Type B MIMO receivers is not able to use adaptive rank, precoding index, modulation, and code rate and, as a result, performance of a wireless MIMO communications system using a Type B MIMO receiver is less than desirable.
Therefore, there is a need for systems and methods for improving the performance (e.g., throughput) of a wireless MIMO communications system using a MIMO receiver employing a Type B (i.e., list-based) MIMO detector.